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Dysregulated RNA splicing induces regeneration failure in alcohol-associated liver disease

GSE281574 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 12 samples Submitted 2025/06/30 Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 12 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Samples
12 samples: 12 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Dysregulated RNA splicing impairs regeneration in alcohol-associated liver diseaseNature communications · 2025
Summary (written by the submitters)
Individuals with progressive liver failure due to alcohol-related liver disease (ALD) are at a high mortality risk without liver transplantation. However, our understanding of derailed regenerative responses in ALD is limited. Here, we performed comprehensive multi-omic profiling of healthy and diseased human livers using single nucleus RNA- plus ATAC-seq. We report that hepatic immune milieu alterations prevent hepatocytes from transitioning to a proliferative progenitor-like state, trapping them in a non-functional intermediate state.
Published in
Dysregulated RNA splicing impairs regeneration in alcohol-associated liver disease
Chembazhi UV, Bangru S, Dutta RK et al. · Nature communications 2025 · PMID 40931030 · doi:10.1038/s41467-025-63251-2
This dataset
Open GSE281574 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE281574_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1184747 and SRA study SRP544485. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40931030) ↗
Study design
12 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 12 sample titles: 12 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
Datasets on a similar topic

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